Data Synergy between Altimetry and L-Band Passive Microwave Remote Sensing for the Retrieval of Sea Ice Parameters-A Theoretical Study of Methodology

作者：Xu, Shiming^*; Zhou, Lu; Liu, Jiping; Lu, Hui; Wang, Bin

来源：Remote Sensing, 2017, 9(10): 1079.

DOI：10.3390/rs9101079

• sea ice
• passive microwave remote sensing
• altimetry
• brightness temperature
• retrieval

Accurate knowledge of the sea ice parameters, including the thickness and the snow depth over sea ice, are key to both climate change studies and operational forecast in polar regions. The estimation of these parameters mainly relies on satellite based remote sensing, and current retrieval algorithms usually focus on the retrieval of a single parameter under simple assumptions over the other. In this article, we explore the potential of combined retrieval of both sea ice thickness and snow depth through the data synergy two types of concurrent observations of the sea ice cover: the active altimetry and the L-band passive remote sensing. The data synergy is based on two physical constrains: (1) L-band (1.4 GHz) radiation model for the sea ice cover, and (2) the hydrostatic equilibrium as used in satellite altimetry. Two schemes of data synergy are proposed: (1) the synergy between L-band brightness temperature (TB) from passive microwave remote sensing and sea ice freeboard (FBice) as measured by radar altimetry, and (2) the synergy between L-band TB and snow freeboard (FBsnow) as measured by laser altimetry. Based on retrievability studies, we show that both parameters can be retrieved using the two sets of data. Specifically, we show that there is potential problem of ill-posedness for the synergy between L-band TB and FBsnow, with two possible retrieval solutions for a small portion of the solution space. On the other hand, the synergy between L-band TB and FBice is always well-posed. In terms of sensitivity, lower uncertainty is witnessed for thin ice for the retrieval with FBice, while the retrieval with FBsnow shows advantage for thick ice. Besides the input parameters of TB, FBice and FBsnow, the uncertainty associated with certain model parameters such as snow and ice densities is not negligible for the uncertainty estimation of the retrieved parameters. Verification is carried out with observational data from Operation IceBridge (OIB) campaigns and SMOS satellite, showing that both sea ice thickness and snow depth can be attained by the proposed retrieval algorithms. These algorithms serve as the basis for large-scale retrieval with satellite remote sensing data, including concurrent observation of the Arctic Ocean by independent satellite campaigns such as SMOS, CryoSat-2 and ICESat.

• 出版日期2017-10
• 单位清华大学; 中国科学院大气物理研究所